5,321 research outputs found
Scale And Translation Invariant Collaborative Filtering Systems
Collaborative filtering systems are prediction algorithms over sparse data sets of user preferences. We modify a wide range of state-of-the-art collaborative filtering systems to make them scale and translation invariant and generally improve their accuracy without increasing their computational cost. Using the EachMovie and the Jester data sets, we show that learning-free constant time scale and translation invariant schemes outperforms other learning-free constant time schemes by at least 3% and perform as well as expensive memory-based schemes (within 4%). Over the Jester data set, we show that a scale and translation invariant Eigentaste algorithm outperforms Eigentaste 2.0 by 20%. These results suggest that scale and translation invariance is a desirable property
Predicting human preferences using the block structure of complex social networks
With ever-increasing available data, predicting individuals' preferences and
helping them locate the most relevant information has become a pressing need.
Understanding and predicting preferences is also important from a fundamental
point of view, as part of what has been called a "new" computational social
science. Here, we propose a novel approach based on stochastic block models,
which have been developed by sociologists as plausible models of complex
networks of social interactions. Our model is in the spirit of predicting
individuals' preferences based on the preferences of others but, rather than
fitting a particular model, we rely on a Bayesian approach that samples over
the ensemble of all possible models. We show that our approach is considerably
more accurate than leading recommender algorithms, with major relative
improvements between 38% and 99% over industry-level algorithms. Besides, our
approach sheds light on decision-making processes by identifying groups of
individuals that have consistently similar preferences, and enabling the
analysis of the characteristics of those groups
Recommender Systems by means of Information Retrieval
In this paper we present a method for reformulating the Recommender Systems
problem in an Information Retrieval one. In our tests we have a dataset of
users who give ratings for some movies; we hide some values from the dataset,
and we try to predict them again using its remaining portion (the so-called
"leave-n-out approach"). In order to use an Information Retrieval algorithm, we
reformulate this Recommender Systems problem in this way: a user corresponds to
a document, a movie corresponds to a term, the active user (whose rating we
want to predict) plays the role of the query, and the ratings are used as
weigths, in place of the weighting schema of the original IR algorithm. The
output is the ranking list of the documents ("users") relevant for the query
("active user"). We use the ratings of these users, weighted according to the
rank, to predict the rating of the active user. We carry out the comparison by
means of a typical metric, namely the accuracy of the predictions returned by
the algorithm, and we compare this to the real ratings from users. In our first
tests, we use two different Information Retrieval algorithms: LSPR, a recently
proposed model based on Discrete Fourier Transform, and a simple vector space
model
A Bayesian Approach toward Active Learning for Collaborative Filtering
Collaborative filtering is a useful technique for exploiting the preference
patterns of a group of users to predict the utility of items for the active
user. In general, the performance of collaborative filtering depends on the
number of rated examples given by the active user. The more the number of rated
examples given by the active user, the more accurate the predicted ratings will
be. Active learning provides an effective way to acquire the most informative
rated examples from active users. Previous work on active learning for
collaborative filtering only considers the expected loss function based on the
estimated model, which can be misleading when the estimated model is
inaccurate. This paper takes one step further by taking into account of the
posterior distribution of the estimated model, which results in more robust
active learning algorithm. Empirical studies with datasets of movie ratings
show that when the number of ratings from the active user is restricted to be
small, active learning methods only based on the estimated model don't perform
well while the active learning method using the model distribution achieves
substantially better performance.Comment: Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004
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